Modeling haul-out behavior of walruses in Bering Sea ice
Bibliographic record
Abstract
Understanding haul-out behavior of ice-associated pinnipeds is essential for designing and interpreting population surveys and for assessing effects of potential changes in their ice environments. We used satellite-linked transmitters to obtain sequential information about location and haul-out state for Pacific walruses, Odobenus rosmarus divergens (Illiger, 1815), in the Bering Sea during April of 2004, 2005, and 2006. We used these data in a generalized mixed model of haul-out bout durations and a hierarchical Bayesian model of haul-out probabilities to assess factors related to walrus haul-out behavior, and provide the first predictive model of walrus haul-out behavior in sea ice habitat. Average haul-out bout duration was 9 h, but durations of haul-out bouts tended to increase with durations of preceding in-water bouts. On average, tagged walruses spent only about 17% of their time hauled out on sea ice. Probability of being hauled out decreased with wind speed, increased with temperature, and followed a diurnal cycle with the highest values in the evening. Our haul-out probability model can be used to estimate the proportion of the population that is unavailable for detection in spring surveys of Pacific walruses on sea ice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".